Relevant bibliographies by topics / Odlehlé hodnoty

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Academic literature on the topic 'Odlehlé hodnoty'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Dissertations / Theses on the topic "Odlehlé hodnoty"

1

Nechutová, Vendula. "Analýza chemických vazeb ve slitinách pomocí dolování dat." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-402554.

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The thesis deals with aplication of data mining methods for the analysis of two Ni3Si supercells, one with a stable grain boundary and the second one with unstable grain boundary. DOS and COHP curves are examined using selected curve matching methods. The surroundings of the individual atoms are examined by the Voronoi diagram. This information was used to reveal the differences in binding between stable and unstable supercell.
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2

Hrabec, Pavel. "Zavedení a aplikace obecného regresního modelu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-232177.

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This thesis sumarizes in detail general linear regression model, including testing statistics for coefficients, submodels, predictions and mostly tests of outliers and large leverage points. It describes how to include categorial variables into regression model. This model was applied to describe saturation of photographs of bread, where input variables were, type of flour, type of addition and concntration of flour. After identification of outliers it was possible to create mathematical model with high coefficient of determination, which will be usefull for experts in food industry for preliminar identification of possible composition of bread.
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3

Zamazal, Petr. "Statistická analýza rozsáhlých dat z průmyslu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445466.

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This thesis deals with processing of real data regarding waste collection. It describes select parts of the fields of statistical tests, identification of outliers, correlation analysis and linear regression. This theoretical basis is applied through the programming language Python to process the data into a form suitable for creating linear models. Final models explain between 70 \% and 85 \% variability. Finally, the information obtained through this analysis is used to specify recommendations for the waste management company.
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4

Výtvar, Jaromír. "Dolovací modul systému pro dolování z dat na platformě NetBeans." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-235551.

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The aim of this work is to get basic overview about the process of obtaining knowledge from databases - datamining and to analyze the datamining system developed at FIT BUT on the NetBeans platform in order to create a new mining module. We decided to implement a module for mining outliers and to extend existing regression module with multiple linear regression using generalized linear models. New methods using existing methods of Oracle Data Mining.
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5

Pešek, Martin. "Získávání znalostí z časoprostorových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237048.

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This thesis deals with knowledge discovery in spatio-temporal data, which is currently a rapidly evolving area of research in information technology. First, it describes the general principles of knowledge discovery, then, after a brief introduction to mining in the temporal and spatial data, it focuses on the overview and description of existing methods for mining in spatio-temporal data. It focuses, in particular, on moving objects data in the form of trajectories with an emphasis on the methods for trajectory outlier detection. The next part of the thesis deals with the process of implementation of the trajectory outlier detection algorithm called TOP-EYE. In order to testing, validation and possibility of using this algorithm is designed and implemented an application for trajectory outlier detection. The algorithm is experimentally evaluated on two different data sets.
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